HPV positive OPSCC carries a better prognosis compared to HPV negative OPSCC. One of the challenges is to elicit which signaling pathways play a key role in tumorigenesis of HPV related OPSCC. Recently, exome sequencing has identified well characterized somatic mutations in HNSCC that are predictive of outcome. Despite it being a popular tool for identifying driver mutations, exome sequencing fails to capture the complexity of changes in gene expression or identifying gene fusions that may be more therapeutically relevant. Recently, we performed DNA sequencing using ion Torrent AmpliSeq panel analysis to detect mutations and a nano Striping's cancer reference panel for gene expression profiling using formalin fixed and paraffin embedded (FFPE) samples from OPSCC cases with HPV-positive negative disease. Our preliminary analysis revealed significant distinguished expression signatures in apoptosis pathway between the HPV positive and negative samples. Based on our preliminary data, we hypothesize that RNA-Seq, a genomics based approach, is superior to exome seq for identifying therapeutically actionable mutations and gene expression signatures in HPV-positive and negative OPSCC and can therefore better help optimizing therapy for patients with OPSCC. We are therefore interested as well in correlating these signatures and mutations with clinical variables using archival FFPE samples from our oropharyngeal database. This study will allow a better stratification of patients with OPSCC and better understanding of the biology of this disease. Therefore, our studies are highly translational and could impact future patient care. Two specific aims will help us achieve our goals. Specific Aim1 will optimize a combined RNA-seq and WES approach for identifying actionable mutations and gene expression signatures in HPV-positive versus HPV-negative OPSCC using 30 archival FFPE samples selected from our OPSCC database. Specific Aim 2 will build a robust and efficient workflow to confirm mutations, copy number abnormalities and differential gene expression in OPSCC and will differentiate the prevalence of gene expression signatures and driver mutations in HPV-positive and negative OPSCC and correlate these with clinical variables using our database.